Domain-independent architecture in a command and control system

ABSTRACT

In one aspect, a command and control (C2) system includes a domain-independent architecture comprising an ontology model. The ontology model includes a resource aspect configured to receive entities specific to a domain, a responsibility aspect configured to receive actions specific to the domain and performed by the entities, a rules aspect configured to receive rules specific to the domain and associated with the actions and a results aspect configured to receive effects specific to the domain and associated with the actions.

RELATED APPLICATIONS

This patent application claims priority to Application Ser. No.60/938,481, filed May 17, 2007 entitled “DOMAIN INDEPENDENT ARCHITECTUREFOR SITUATIONAL UNDERSTANDING” which is incorporated herein in itsentirety.

BACKGROUND

Command and control (C2) relates to decision-making and the individualswho make decisions. C2 is an ability to recognize what needs to be donein a situation and ensures that effective actions are taken to achieveobjectives. In one example, in the military environment, a commander isresponsible for C2.

Ontology is a formal explicit specification of concepts in a domain anda relationship among the concepts. For example, the ontology of a domainfor a pizza would include concepts of the pizza, a pizza base, and apizza topping. Subconcepts of the pizza base include deep pan base andthin and crispy base. Subconcepts of the pizza topping include a cheesetopping, a vegetable topping and a meat topping. A relationship amongthe concepts includes, for example, that all pizza has one pizza baseand one or more pizza topping.

SUMMARY

In one aspect, a command and control (C2) system includes adomain-independent architecture comprising an ontology model. Theontology model includes a resource aspect configured to receive entitiesspecific to a domain, a responsibility aspect configured to receiveactions specific to the domain and performed by the entities, a rulesaspect configured to receive rules specific to the domain and associatedwith the actions and a results aspect configured to receive effectsspecific to the domain and associated with the actions.

In another aspect, a command and control (C2) system includes adomain-independent architecture that includes an ontology model. Theontology model includes a resource aspect configured to receive entitiesspecific to a domain, a responsibility aspect configured to receiveactions specific to the domain and performed by the entities, a ruleaspect configured to receive rules specific to the domain and associatedwith the actions and a result aspect configured to receive effectsspecific to the domain and associated with the actions. Each of theaspects includes at least four levels of abstraction to form at leastsixteen concepts and each of the at least four levels of abstractionincludes a number of concepts corresponding to a number of aspects. Eachof the concepts comprises at least one relationship with anotherconcept. The at least one relationship with another concept includes atleast one relationship with another concept in the same level ofabstraction and at least one relationship with at least one concept inthe same aspect.

In a further aspect, a method of forming a command and control (C2)system includes providing a domain-independent architecture comprisingan ontology model including providing a resource aspect configured toreceive entities specific to a domain, providing a responsibility aspectconfigured to receive actions specific to the domain and performed bythe entities, providing a rule aspect configured to receive rulesspecific to the domain and associated with the actions and providing aresult aspect configured to receive effects specific to the domain andassociated with the actions. Each of the aspects includes at least fourlevels of abstraction to form at least sixteen concepts and each of theat least four levels of abstraction comprises a number of conceptscorresponding to a number of aspects. Each of the concepts comprises atleast one relationship with another concept includes at least onerelationship with another concept in the same level of abstraction andat least one relationship with at least one concept in the same aspect.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a command and control (C2) system including adomain-independent architecture.

FIG. 2 is a block diagram of an example of an ontology model includingfour aspects.

FIG. 3 is a block diagram of an example of an ontology model includingfour levels of abstraction.

FIGS. 4A to 4D are horizontal relationship diagrams between aspects.

FIG. 5 is a block diagram depicting example relationships amongst theconcepts in the ontology model.

FIG. 6 is a command and control (C2) system of FIG. 1 tailored to adomain-specific environment.

FIG. 7 is a flowchart of a process to generate a C2 system for a domainfrom the c2 system of claim 1.

FIG. 8 is a flowchart of a process to provide C2.

FIG. 9 is a block diagram of an example of a computer on which theprocess of FIG. 8 may be implemented.

DETAILED DESCRIPTION

Complex missions involving many geographically dispersed people andsystems challenge human ability to keep track of what is happening. Attimes, the amount of data is overwhelming, making it difficult to focusand act on the important pieces of information. Also, the informationcan be incomplete or ambiguous, thus making decisions even moredifficult. Given enough time and opportunity to assimilate information,people are uniquely adapted to understanding situations and to makenecessary decisions. However, when adequate time or manpower isunavailable, loosely coupled actors can quickly become unsynchronized,leading to mission failure.

As used herein a mission may apply to a number of scenarios. In oneexample, a mission may apply to a military scenario. In another example,the mission may apply to natural disasters such as preparation andrelief. In another example, the mission may apply to operations of acompany.

As used herein raw data is defined as what is observed (e.g., detected,sensed and so forth). Resource data (information) is defined as what isprocessed (i.e., derived from the observations). Knowledge data isdefined as how what is observed and derived from observations iscontextualized (i.e., explained).

Described herein is a domain-independent command and control (C2)architecture that may be used in any type of domain environment toaccomplish a mission. The C2 architecture may be applied to include allcommand and control problems, customized through an ontology model. Theform of the ontology model can represent a complete set of knowledgenecessary to support C2. By using the ontology model, knowledge aboutthe mission is dynamically built and reasoned about. The dynamicreasoning leads to just-in-time, contextually relevant andindividualized decision support for C2. The domain-independent C2architecture represents knowledge for sharing and collaboration, whichcan improve both individual and shared awareness and improvedecision-making, thereby leading to mission effectiveness and agility.

By populating the domain-independent C2 knowledge framework withspecific instances of knowledge from a desired domain, thedomain-independent architecture can be tailored to accomplish C2functionality for that domain. Thus, any domain can use thedomain-independent C2 architecture domain to support C2. For example,the domain-independent C2 architecture may be used in militaryoperations, law enforcement operations, disaster relief operations andso forth.

The domain-independent C2 architecture includes domain-independentreasoning engines that automatically and constantly organize and relateinformation, interpret its implication to command and control and, basedon the interpretation, provide role-customized decision support. Thedomain-independent C2 architecture provides dynamic information flowmanagement so that the right information is available to the rightperson at the right time, real-time synchronization of independent,asynchronous actors so that everyone works together towards the samegoals and objectives and provides a common assessment of events so thatall decisions are based on a shared understanding of the situation.

The domain-independent C2 architecture allows for missions on a largescale and scope to be performed by assisting in organizing and relatinginformation, tracking what has happened, anticipating future actions andproviding just-in-time support so that that necessary decision-makingcan be made. Such systems would accept input from sensors, understandthe value of the data; integrate the data with data from other sensorsif necessary and forward the composite information to those who need it.Likewise, the system would accept input from people, understand therelevance of the input, integrate with the input from others ifnecessary and share the composite information with those who need it.The system can further prompt people and systems into actions that arerequired by the situation.

Referring to FIG. 1, a command and control system 10 includes clients(e.g., a client 12 a and a client 12 b), client interfaces (a clientinterface 16 a and a client interface 16 b), a service orientedarchitecture framework 22, a decision support system 24 and an ontologymodel 50. The decision support system 24 includes resource agents (e.g.,a resource agent 26 a and a resource agent 26 b), knowledge agents 32that use the ontology model 50 and domain-independent reasoning agents(e.g., a domain-independent reasoning agent 36 a and adomain-independent reasoning agent 36 b). In one example, system 10 is anet-centric solution that combines information received from multiplesources to continuously generate situational knowledge of who is where,doing what, how, why and what might happen next. Based on thesituational knowledge, the system 10 provides just-in-time,role-customized information and advice to the clients 12 a, 12 b.

The clients 12 a, 12 b may include systems and people that gather dataand/or consume data. For example, the clients 12 a, 12 b may be a sensorsuch as a radar, a satellite, a radiation detector and so forth. Inanother example, the clients 12 a, 12 b may be a person entering data ora system that provides data. In other examples, the clients 12 a, 12 bmay be persons or systems that use the data. The clients 12 a, 12 b maybe a decision-maker. For example, a client 12 a may receive C2 data 48.

The client interfaces 16 a, 16 b are automatically customized by theclient's role and current situation based on the specific domain. In oneexample, the client interfaces 16 a, 16 b include a web-based clientinterface, which is dynamically composed and includes multiple ways torender information (e.g., rendering information as maps, charts, tables,graphs, texts and so forth). Each rendering depicts consistentinformation, because each rendering is derived from a common repositoryof situational knowledge. The client interfaces 16 a, 16 b provide rawdata 42 to the service-oriented architecture framework 22.

The service-oriented architecture framework 22 includes, for example, anenterprise bus for exchanging information and databases to collectinformation and store synthesized knowledge. In one example, the serviceoriented architecture framework 22 includes a Java 2 Platform,Enterprise Edition (J2EE) framework, which is suitable for nearreal-time services. In one example, data models are implemented in theframework 22 to capture mission scale data. The data models may includedata models to describe sensor data, geo-physical data, weather data,material request, material availability and logistics, weapons control,resource availability, movement, relationships among mission entitiesand so forth. In other examples, adapters are implemented in theframework 22 to integrate data in existing databases such as, forexample, the Global Information Grid (GIG), intelligence databasesthrough the Distributed Common Ground System (DCGS) Integration Backbone(DIB) manufactured by Raytheon Company of Waltham, Mass., logisticsdatabases, transportation databases and so forth.

The resource agents 26 a, 26 b are implemented, for example, one perclient 12 a, 12 b (e.g., client 12 a is associated with resource agent26 a and client 12 b is associated with resource 26 b). The resourceagents 26 a, 26 b interpret raw data 42 received from its associatedclient 12 a, 12 b; and customize knowledge and presentation to meetrequirements of the clients 12 a, 12 b. In one example, the resourceagents 26 a, 26 b dynamically compose text and graphical outputs for itsassociated client 12 a, 12 b based on situational knowledge. In oneexample, the resource agents 26 a, 26 b customize human-computerinteraction to enhance cognition. In one example, the resource agents 26a, 26 b process the raw data 42 to form resource data 44.

The knowledge agents 32 receive the resource data 44 from the resourceagents 26 a, 26 b and generate a common knowledge of who is doing what,why, how and what happens next. The knowledge agents 32 use the domainknowledge in the ontology model 50 to interpret inputs of the clients 12a, 12 b and infer who is doing what and why. For example, the knowledgeagents 32 link pieces of information per the ontology model 50 andfurther links the pieces to possible predicted future outcomes to relatepast events to anticipated ones.

The knowledge agents 32 interpret the data received and relate it toevents defined in the ontology model 50 to form knowledge data 42. Asconditions for an event unfold (as defined in the ontology model 50),the knowledge agents 32 infer that an event has happened and identifyevents that are stated in the ontology model 50 as likely to follow andseek data indicating the anticipated events. For example, the ontologymodel 50 maps events to observations/indications of the event and toactors who perform the observations. Situational knowledge is formedfrom a process of seeking data, associating the data with events,reporting events in progress or complete and identifying future events.

The domain-independent reasoning agents 36 a, 36 b use the knowledgedata 42 (e.g., situational knowledge) generated by the knowledge agents32 to apply algorithms to answer specific questions such as, forexample, “what are recommended course of actions,” “who should receivethis information” and so forth. The results of the analysis by thereasoning agents 36 a, 36 b is provided to the clients 12 a, 12 b as adecision-maker or may be used for autonomous actions. In one example,the reasoning agents 32 provide role customized decision support for JDLfusion levels 2 to 5.

In one example, the domain-independent reasoning agents 36 a, 36 bconstantly monitor the situational knowledge (e.g., knowledge data 42)and seek information (e.g., resource data 44 from resource agents 26 a,26 b) required to conduct their respective portion of reasoning, whichinvolves, for example, combining associated information to deriveadditional information. The additional information is reported back tothe knowledge agents 32. In one example, if the new information is partof a decision support capability, then the domain-independent reasoningagent 36 a or 36 b also reports it to the correspondingdomain-independent resource agent 26 a or 26 b.

The domain-independent reasoning agents 36 a, 36 b perform reasoningthat is required in situations that may occur in the mission. Examplesof functions performed by the domain-independent reasoning agents 36 a,36 b include correlating information to identify the occurrence of acertain event, correlating a collection of events to anticipate a likelysituation, inferring cause and effects, maintaining a history of pastevents, anticipating likely events in the near future, forecastingworkflow and information flow, determining location, status and identityof systems and people, forecasting the intent of an enemy or a threat,forecasting risk, determining the alignment between the actors and thecommander's intent, identifying viable strategy, identifying coursecorrection, planning execution and tasking resources. For example, thereasoning agents 36 a, 36 b use fundamental, domain-independentrelationships in the ontology model 50 between actors, activities andresults to make inferences. In one example, all the domain knowledgenecessary to perform the reasoning is contained in the ontology model50.

In one example, system 10 provides automatic cataloging of data,establishing relationship among data, and fitting data to a commoncontext and identifying data needed to disambiguate context.

In one example, system 10 provides just-in-time, individualized andcontext relevant decision support, which saves time. System 10 alsoprovides prompting for action, packaging of relevant information,suggested collaborations, automating information flow and automatingworkflow.

Referring to FIG. 2, the ontology model 50 includes a responsibilityaspect 52, a resource aspect 54, a rule aspect 56 and a result aspect58. The four aspects 52-58 align diverse data so that information can besynthesized, aligns people with the information so that knowledge can besynthesized and represents knowledge so that actionable understandinghappens. The ontology model is stored, for example, on a storage medium.In one example, the ontology model 50 may be represented in the WebOntology Language (OWL). In one example, the ontology model 50 is adatabase structure.

The aspects 52-58 form the cohesion for generating actionableunderstanding. The resource aspect 54 includes entities that performactions (e.g., “who is responsible?”). The responsibility aspect 52includes the actions performed by the entities (e.g., “what actionshappen?”). The rule aspect 56 identifies constraints on the actions(e.g., “what rules govern the action?”). The result aspect 58 includes astate change associated with the actions being performed (e.g., “what isthe result of the action?”).

The ontology model 50 provides logical relationships among the aspects52-58. At run-time, real-time information are linked to each other perthe relationships in the ontology model 50 to build real-timesituational knowledge, which in turn trigger software agents to promptactions, customize data packages and provide situation specific decisionsupport.

Referring to FIG. 3, the aspects 52-58 are further classified into atleast four levels of abstractions (or perspectives) to capture andrelate concepts all the way from mission level perspectives toactivities of individual actors to form sixteen concepts, four conceptsfor each aspect 52-58. In other examples of the ontology model 50′, thesixteen concepts may be further sub-divided as necessary to specifyother relationships concepts at finer levels of abstraction.

For example, at a first level of abstraction 62, the responsibilityaspect 52 includes a mission concept 52 a associated with one or moremissions, the resources aspect 54 includes an owner concept 54 aassociated with one or more owners, the rule aspect 56 includes a lawconcept 56 a associated with one or more laws and the result aspect 58includes an objective aspect 58 a associated with one or moreobjectives.

At a second level of abstraction 64, the responsibility aspect 52includes an operation concept 52 b associated with one or moreoperations, the resource aspect 54 includes an organization concept 54 bassociated with one or more organizations, the rule aspect 56 includes adoctrine concept 56 c associated with one or more doctrines and theresult aspect 58 includes an end-state concept 58 d associated with oneor more end-states.

At a third level of abstraction 66, the responsibility aspect 52includes a task concept 52 c associated with one or more tasks, theresource aspect 54 includes an actor concept 54 c associated with one ormore actors, the rule aspect 56 includes a procedure concept 56 cassociated with one or more procedures and the result aspect 58 includesa production concept 58 c associated with one or more productions.

At a fourth level of abstraction 68, the responsibility aspect 52includes an activity concept 52 d associated with one or moreactivities, the resource aspect 54 includes a behavior concept 54 dassociated with one or more behaviors, the rule aspect 56 includes atechnique concept 56 d associated with one or more techniques and theresult aspect 58 includes an information concept 58 d associated withone or more bits of information. In other examples, other levels ofabstraction can be added in a symmetric fashion as needed.

Each level of abstraction 62-68 provides a greater level of granularityso that the level of granularity of the fourth level 68 is higher thanthe other levels 62-66. For example, with respect to the concepts 52a-52 d, a mission includes operations, which includes tasks, whichincludes activities.

There are two kinds of relationships between the concepts: verticalrelationships 72 and horizontal relationships 82. Vertical relationships72 are relationships between concepts from two adjacent levels ofabstraction within a single aspect. For example, the mission concept 52a and the operation concept 52 b form a vertical relationship 72 and thetask concept 52 c and the activity concept 52 d form a verticalrelationship.

In vertical relationships 72 a higher level of abstraction conceptincludes the concepts in the next lower level of abstraction immediatelybelow and visa versa. For example, the mission concept 52 a includes theoperation concepts 52 b and the operation concept 52 b is included inthe mission concept 52 a. The vertical relationships 72 are alsotransitive. For example, if a concept A includes a concept B and conceptB includes a concept C, then concept A also includes concept C. Thus,the vertical relationships 72 may be nested at various levels ofabstraction within each other.

Horizontal relationships 82 are relationships between concepts ofdifferent aspects within a single level of abstraction. For example, themission concept 52 a, the owner concept 54 a, the law concept 56 a andthe objective concept 58 a have a horizontal relationship 82 with eachother.

Referring to FIGS. 4A to 4D, the horizontal relationships 82 relateaspects 52-58 but within a single level of abstraction 62-68. Thehorizontal relationships 82 pair-wise relate aspects 52-58 and are alsosymmetrical. In the example shown in FIGS. 4A to 4D there are twelveexample horizontal relationships 102-124. Each level of abstractiondefines the twelve fundamental relationships among its four aspectconcepts.

For example, with respect to the responsibility aspect 52, there is arelationship 102 between the responsibility aspect and the resourceaspect 54 by a property “has resource,” there is a relationship 104between the responsibility aspect and the rule aspect 56 by a property“has rule” and there is a relationship 104 between the responsibilityaspect and the results aspect 56 by a property “has result” (FIG. 4A).With respect to the resource aspect 54, there is a relationship 108between the resource aspect and the responsibility aspect 52 by aproperty “satisfies responsibility,” there is a relationship 110 betweenthe resource aspect and the rule aspect 56 by a property “satisfiesrule” and there is a relationship 112 between the responsibility aspectand the results aspect 56 by a property “satisfies result” (FIG. 4B).With respect to the result aspect 58, there is a relationship 114between the result aspect and the responsibility aspect 52 by a property“caused by responsibility,” there is a relationship 116 between theresult aspect and the rule aspect 56 by a property “caused by rule” andthere is a relationship 118 between the result aspect and the resourceaspect 54 by a property “caused by resource” (FIG. 4C). With respect tothe rule aspect 56, there is a relationship 120 between the rule aspectand the responsibility aspect 52 by a property “applies toresponsibility,” there is a relationship 122 between the rule aspect andthe resource aspect 58 by a property “applies to resource” and there isa relationship 124 between the rule aspect and the results aspect 56 bya property “applies to result” (FIG. 4D).

The twelve fundamental horizontal relationships 102-124 include sixindependent relationships, each independent relationship having a directrelationship and a reciprocal relationship. For example, therelationship 102 is reciprocal to the relationship 108, the relationship104 is reciprocal to the relationship 120, the relationship 106 isreciprocal to the relationship 114, the relationship 108 is reciprocalto the relationship 122, the relationship 112 is reciprocal to therelationship 118 and the relationship 116 is reciprocal to therelationship 124.

In other examples, there may be additional or other horizontalrelationships 82 within the aspects at a same level of abstraction. Forexample, an actor concept 54 c may be related to another actor conceptby a property, “has Partner”. In another example, the result concept 58is related to the responsibility aspect 52 by a pair of reciprocalrelationships having one property per pair, for example, a “triggersresponsibility” property and a “triggered by responsibility” property todenote chain reactions.

The collection of vertical relationships 72 and horizontal relationships82 provide a complete connection between any of the concepts (e.g., theconcepts 52 a-52 d, 54 a-54 d, 56 a-56 d, 58 a-58 d (in FIG. 3)) in thedomain-independent C2 ontology. The complete connection allows theknowledge agents 32 to continually generate situational knowledge(knowledge data 42) and the reasoning agents 36 a, 36 b to providecontext-relevant decision support.

System 10 is a knowledge-based fusion system where patterns of behaviorsfor actors in a mission are stored in the ontology model 50′. In theontology model 50′, actions are related to those that precede it andthose that follow. Each action is also described in terms of the datathat it requires and the data that it produces. Actions are paired withactors and consequences. Simple atomic concepts relate actions to actorsand actors to consequences. Many atomic relationships together describea mission. In one example, the data for the ontology model is derivedfrom various operational and systems views in the Department of DefenseArchitecture Framework (DoDAF), for example. Based on the ontology model50′, the knowledge agents 32 combine and recombine resource data 44 togenerate knowledge data 42, to answer C2 related questions, and toprompt for action in real-time.

In one example, the relationships include the following: an activitybelongs to a task, a behavior causes an activity, a behavior follows atechnique, an activity generates an interaction, an owner has a missionand a law governs execution of the mission.

FIG. 5 is an example of the relationships 72, 82 used by one or more ofthe domain-independent reasoning agents 36 a, 36 b to form a situationalunderstanding. For example, by using the horizontal relationships 82 andthe vertical relationships 72, the domain-independent reasoning agents36 a, 36 b infer operational progress using the operation concept 52 b,infer a task being performed from the task concept 52 c, infer an actorusing the actor concept 54 c, infer the data that is produced from theproduction concept 58 c, and identify the actors who produce theinformation, desired end states and the production needed. Theinferences are spontaneously and continuously made in no prescribedorder.

Referring to FIG. 6, a C2 system 10′ for a specific domain may be formedfrom the system 10. The C2 system 10′ includes domain-dependentreasoning agents 336 a, 336 b customized to a domain to answerdomain-specific queries. The C2 system 10′ also includes an ontologymodel 350 similar to the ontology model 50 but including specific domaininstances 352. For example, the ontology model 350 is a database and thespecific instances are placed in the fields of the database. In anotherexample, the ontology model 350 is implemented in OWL and populated withdomain-specific instances.

Referring to FIG. 7, the domain-independent architecture in system 10may be tailored to a specific domain using a process 400, for example.Requirements to accomplish a mission are determined (402) and a missionsolution is modeled (406). For example, in a military domain the missionmay include blowing up a bridge or in a disaster recovery domain themission may include removing a chemical spill and protecting thepopulation. The mission and the mission solution may be determined fromtechniques described in application Ser. No. 11/265,802 entitled“MISSION PROFILING” and application Ser. No. 11/392,222 entitled“ADAPTIVE MISSION PROFILING,” each of these two patent applications areincorporated herein in their entirety and each are also assigned to thesame entity as this patent application. In one example, an analyzablemission model is generated identifying the actors in the mission, whataction the actors perform in the mission, when the actors perform theactions, why the actors perform the actions and how the actors performthe actions.

The command and control (C2) functions are determined (410). The stepsin the mission model that requires decisions are identified andisolated. From the mission model, the information that is necessary tomake a decision is identified and the knowledge that will support thosedecisions is identified.

The ontology model 50 is populated with domain-specific instances 352 toform the ontology model 350 (414). For example, each of the sixteenconcepts 52 a-52 d, 54 a-54 d, 56 a-56 d, 58 a-58 d (in FIG. 3) ispopulated in the ontology model 50′ with domain specific data on whomakes what decision, when and how. For example, in a disaster recoverydomain, the organizations such as the police department, the firedepartment and a hazardous materials team are included in theorganization concept of the ontology model 50′. Specifically, concepts52 a-52 d, 54 a-54 d, 56 a-56 d, 58 a-58 d are tailored to the domain(for example, policeman and firefighters are actors in the actor concept54 c).

The domain-specific reasoning agents 336 a, 336 b are implemented (424).For example, the domain-dependent reasoning agents 336 a, 336 b aredesigned to generate the necessary knowledge and prompt the resourceagents 26 a, 26 b appropriately. For example, a domain-dependentreasoning agent 336 a, 336 b to track victim status in an environmentaldisaster would function differently from a domain-dependent reasoningagent for tracking victims in a health epidemic. The ontology model 50and the domain-specific reasoning agents 336 a, 336 b are integrated ina C2 system to form a domain-specific C2 system 10′ (430).

Referring to FIG. 8, an example of a process to provide C2 support for adomain is a process 500. Raw data is received (502) and is processed toform resources data (506). For example, raw data 42 is received from theclients 12 a, 12 b and the resource agents 26 a, 26 b process the rawdata 42 to provide resource data 44.

The resource data is processed (510). For example, resource data 44 isprocessed using the ontology model 350 including the domain-specificinstances 352 to provide the knowledge data 42. For example, thedomain-independent reasoning agents 36 a, 36 b and the domain-dependentreasoning agents 336 a, 336 b using the knowledge data 42 provide C2data 48 based on queries. In one example, the client 12 a may be adecision-maker that make queries for which the reasoning agents 36 a, 36b, 336 a, 336 b solve the queries. In one example, each of theprocessing blocks 502, 506, 510 and 520 are performed continuously,spontaneously, as required or any combination thereof.

Referring to FIG. 9, all or part of the C2 system 10′ may be configuredas a C2 processing system 10″, for example. The C2 system 10″ includes aprocessor 602, a volatile memory 604 and a non-volatile memory 606(e.g., hard disk). The non-volatile memory 626 stores computerinstructions 614, an operating system 610 and data 612. In one example,the computer instructions 614 are executed by the processor 602 out ofvolatile memory 604 to perform the process 500.

Process 500 is not limited to use with the hardware and software of FIG.9; it may find applicability in any computing or processing environmentand with any type of machine or set of machines that is capable ofrunning a computer program. Process 500 may be implemented in hardware,software, or a combination of the two. Process 500 may be implemented incomputer programs executed on programmable computers/machines that eachincludes a processor, a storage medium or other article of manufacturethat is readable by the processor (including volatile and non-volatilememory and/or storage elements), at least one input device, and one ormore output devices. Program code may be applied to data entered usingan input device to perform process 500 and to generate outputinformation.

The system may be implemented, at least in part, via a computer programproduct, (e.g., in a machine-readable storage device), for execution by,or to control the operation of, data processing apparatus (e.g., aprogrammable processor, a computer, or multiple computers)). Each suchprogram may be implemented in a high level procedural or object-orientedprogramming language to communicate with a computer system. However, theprograms may be implemented in assembly or machine language. Thelanguage may be a compiled or an interpreted language and it may bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program may be deployed to be executed on onecomputer or on multiple computers at one site or distributed acrossmultiple sites and interconnected by a communication network. A computerprogram may be stored on a storage medium or device (e.g., CD-ROM, harddisk, or magnetic diskette) that is readable by a general or specialpurpose programmable computer for configuring and operating the computerwhen the storage medium or device is read by the computer to performprocess 500. Process 500 may also be implemented as a machine-readablestorage medium, configured with a computer program, where uponexecution, instructions in the computer program cause the computer tooperate in accordance with process 500.

The processes described herein are not limited to the specificembodiments described. For example, the process 500 is not limited tothe specific processing order of FIG. 8, respectively. Rather, any ofthe processing blocks of FIG. 8 may be re-ordered, combined or removed,performed in parallel or in serial, as necessary, to achieve the resultsset forth above.

The processing blocks in FIG. 8 associated with implementing the systemmay be performed by one or more programmable processors executing one ormore computer programs to perform the functions of the system. All orpart of the system may be implemented as, special purpose logiccircuitry (e.g., an FPGA (field programmable gate array) and/or an ASIC(application-specific integrated circuit)).

Elements of different embodiments described herein may be combined toform other embodiments not specifically set forth above. Otherembodiments not specifically described herein are also within the scopeof the following claims.

1. A command and control (C2) system comprising: a domain-independentarchitecture comprising an ontology model comprising: a resource aspectconfigured to receive entities specific to a domain; a responsibilityaspect configured to receive actions specific to the domain andperformed by the entities; a rule aspect configured to receive rulesspecific to the domain and associated with the actions; and a resultaspect configured to receive effects specific to the domain andassociated with the actions.
 2. The C2 system of claim 1 wherein each ofthe aspects comprises at least four levels of abstraction to form atleast sixteen concepts and each of the at least four levels ofabstraction comprises a number of concepts corresponding to a number ofaspects, wherein each of the concepts comprises at least onerelationship with another concept.
 3. The C2 system of claim 2 whereinthe at least one relationship with another concept comprises at leastone relationship with another concept in the same level of abstraction.4. The C2 system of claim 2 wherein the at least one relationship withanother concept comprises at least one relationship with at least oneconcept in the same aspect.
 5. The C2 system of claim 1, furthercomprising a knowledge agent configured to receive data and configuredto provide knowledge data based on the ontology model to generatesituational knowledge of a mission using the resource aspect, the ruleaspect, the result aspect and the responsibility aspect.
 6. The C2system of claim 5, further comprising a resource agent configured toprovide resource data to the knowledge agent.
 7. The C2 system of claim6 wherein the knowledge agent provides the knowledge data to acorresponding resource agent.
 8. The C2 system of claim 6, furthercomprising a domain-independent reasoning agent configured to receivethe knowledge data from the knowledge agent.
 9. The C2 system of claim 8wherein the domain-independent reasoning agent is configured to processthe knowledge data generated by the knowledge agent to provide C2 datato answer C2 queries.
 10. The C2 system of claim 1 wherein the domain isfrom a group of domains consisting of a military domain, a disasterpreparedness domain, a disaster response domain and a corporate businessdomain.
 11. The C2 system of claim 1 wherein the ontology model ispopulated with domain-specific instances; and further comprising adomain-dependent reasoning agent associated with the domain configuringto customize C2 data provided by a domain-independent reasoning agentusing the domain-specific instances.
 12. The C2 system of claim 1wherein the ontology model is represented in the Web Ontology Language(OWL).
 13. The C2 system of claim 1 wherein the ontology model isrepresented in a database.
 14. A command and control (C2) systemcomprising: a domain-independent architecture comprising an ontologymodel comprising: a resource aspect configured to receive entitiesspecific to a domain; a responsibility aspect configured to receiveactions specific to the domain and performed by the entities; a ruleaspect configured to receive rules specific to the domain and associatedwith the actions; and a result aspect configured to receive effectsspecific to the domain and associated with the actions. wherein each ofthe aspects comprises at least four levels of abstraction to form atleast sixteen concepts and each of the at least four levels ofabstraction comprises a number of concepts corresponding to a number ofaspects, wherein each of the concepts comprises at least onerelationship with another concept, wherein the at least one relationshipwith another concept comprises: at least one relationship with anotherconcept in the same level of abstraction; and at least one relationshipwith at least one concept in the same aspect.
 15. The C2 system of claim14, further comprising: a knowledge agent configured to receive resourcedata and configured to provide knowledge data based on the ontologymodel to generate situational knowledge of a mission using the resourceaspect, the rule aspect, the result aspect and the responsibilityaspect; a resource agent configured to provide the resource data to theknowledge agent; a domain-independent reasoning agent configured toprocess the knowledge data from the knowledge agent to provide C2 datato answer C2 queries.
 16. The C2 system of claim 15 wherein the domainis from a group of domains consisting of a military domain, a disasterpreparedness domain, a disaster response domain and a corporate businessdomain.
 17. The C2 system of claim 16 wherein the ontology model ispopulated with domain-specific instances; and further comprising adomain-dependent reasoning agent associated with the domain configuringto customize C2 data provided by a domain-independent reasoning agentusing the domain-specific instances.
 18. The C2 system of claim 17wherein the ontology model is represented in the Web Ontology Language(OWL).
 19. A method of forming a command and control (C2) system, themethod comprising: providing a domain-independent architecturecomprising an ontology model comprising: providing a resource aspectconfigured to receive entities specific to a domain; providing aresponsibility aspect configured to receive actions specific to thedomain and performed by the entities; providing a rule aspect configuredto receive rules specific to the domain and associated with the actions;providing a result aspect configured to receive effects specific to thedomain and associated with the actions; wherein each of the aspectsincludes at least four levels of abstraction to form at least sixteenconcepts and each of the at least four levels of abstraction comprises anumber of concepts corresponding to a number of aspects, wherein each ofthe concepts comprises at least one relationship with another concept,wherein the at least one relationship with another concept comprises: atleast one relationship with another concept in the same level ofabstraction; and at least one relationship with at least one concept inthe same aspect.
 20. The method of claim 19, further comprising:configuring a knowledge agent to receive resource data and configured toprovide knowledge data based on the ontology model to generate asituational knowledge of a mission using the resource aspect, the ruleaspect, the result aspect and the responsibility aspect; configuring aresource agent to provide the resource data to the knowledge agent; andconfiguring a domain-independent reasoning agent to process theknowledge data from the knowledge agent to provide C2 data to answer C2queries.
 21. The method of claim 20, further comprising: populating theontology model with domain-specific instances; and configuring adomain-dependent reasoning agent associated with the domain to customizethe C2 data provided by the domain-independent reasoning agent using thedomain-specific instances in the ontology model, wherein the domain isfrom a group of domains consisting of a military domain, a disasterpreparedness domain, a disaster response domain and a corporate businessdomain.